1. Field of the Invention
The present application is direct to a method and system for managing and implementing one or more external code sets using an interface terminology.
2. Description of the Related Art
One of the challenges facing healthcare computing is the representation of patient data in a usable form. The typical approach is to encode the information using terms taken from a controlled vocabulary. Applications such as CPR's (computer-based patient records), order entry, summary reporting, automated decision support and data aggregation for clinical research all require data to be represented in standard ways if there is to be any meaningful understanding of the data. Understandably, health care providers, educators, researchers, medical and scientific software developers and policy makers often take for granted the existence of an appropriate standard terminology and assume that it is in routine use. In reality, the lack of a standard for representing patient data is one of the today's greatest impediments to medical computing.
The coding of patient information has been carried out long before the advent of computers. This coding typically has been directed at simplifying the data and converting it to a general form that is easier to manipulate and understand. For example, while a patient may have pneumonia that may have been caused by a variety of organisms, involved in different sites in the lung, accompanied by differing symptoms, and of varying levels of severity, coding a patient's diagnosis simply as “bacterial pneumonia” allows it to be aggregated with other cases for statistical purposes, although this coding may lack sufficient specificity for meaningful analysis and treatment.
If finer granularity is needed, more specific terms can be added to the coding scheme (such as gram negative bacterial pneumonia and lobar bacterial pneumonia). A set of patient records can be classified with such codes and then retrieved when cases of certain types are needed. Since this coding represents only a simplified synopsis of information extracted from the record, it may be referred to as abstraction. Record abstraction may be used, e.g., to allow the assessment of incidence of a disease, mortality of a surgical procedure, or cost for a hospital stay.
Documents in a medical field may contain information to which clinical descriptions may be attached, see, e.g., co-owned U.S. publication 2012/0179696, titled “System and Process for Concept Tagging and Content Retrieval.”
As computer use has become more prevalent, electronic health records or electronic medical records (EHRs or EMRs) have become the industry standard for documenting patient care. Industry initiatives and government legislation have facilitated EHR implementation and use. Most notable among them is the Health Information Technology for Economic and Clinical Health Act (HIT ECH), which gives incentives to providers toward implementation and demonstration of meaningful EHR use.
An aspect of reliable and accurate information is ensuring that providers have the ability to capture their clinical intentions regarding patient care through terminologies. Healthcare terminology has long been called “the language of medicine,” but, in the electronic age, this language has to be readable by both humans and computers. Various terminologies are used in defining associated terms.
Terminology
Terminology is a set of descriptions used to represent concepts specific to a particular discipline. It also is the foundation of EHR data. For example, the terms “heart attack” and “MI” describe the same concept of myocardial infarction. The concept in turn may be associated with codes that are used for a variety of purposes.
Different healthcare terminologies may have their own unique features and purposes. For example, one set of terminologies, RxNorm, encodes medications, while another set of terminologies, e.g., Logical Observation Identifiers Names and Codes (referred to under the trademark “LOINC”), is used for laboratory results.
Terms related to terminology include: Administrative code sets; Clinical code sets; and Reference terminologies.
Administrative code sets may be designed to support administrative functions of healthcare, such as reimbursement and other secondary data aggregation. Common examples are the International Classification of Disease (ICD) and the Current Procedural Terminology, which is referred to via the trademark CPT. Each system may be different, e.g., ICD's purpose is to aggregate, group, and classify conditions, whereas CPT is used for reporting medical services and procedures.
Clinical code sets have been developed to encode specific clinical entities involved in clinical work flow, such as LOINC and RxNorm. Clinical code sets have been developed to allow for meaningful electronic exchange and aggregation of clinical data for better patient care. For example, sending a laboratory test result using LOINC facilitates the receiving facility's ability to understand the result sent and make appropriate treatment choices based upon the laboratory result.
A reference terminology may be considered a “concept-based, controlled medical terminology.” The Systematized Nomenclature of Medicine Clinical Terms (referred to under the trademark “SNOMED CT”) is an example of this kind of terminology. It maintains a common reference point in the healthcare industry. Reference terminologies also identify relationships between their concepts. Relationships can be hierarchically defined, such as a parent/child relationship. The reference terminology contains concept A and concept B, with a defined relationship of B as a child of A. SNOMED CT includes concepts such as heart disease and heart valve disorder, and their defined relationship identifies heart valve disorder as a child of heart disease.
Reference terminology may allow healthcare systems to get value from clinical data coded at the point of care. In general, reference terms may be useful for decision support and aggregate reporting and may be more general than the highly detailed descriptions of actual patient conditions. For example, one patient may have severe calcific aortic stenosis and another might have mild aortic insufficiency; however, a healthcare enterprise might be interested in finding all patients with aortic valve disease. The reference terminology creates links between “medical concepts” that allow these types of data queries.
An important aspect of a well-constructed terminology is concept orientation, typically granular by nature and defined as “a unit of knowledge or thought created by a unique combination of characteristics.” An example of a SNOMED CT concept is aortic valve disorder. A concept may have multiple subconcepts arranged in a hierarchical relationship.
Many clinicians are required to use administrative coding sets (CPT, HCPCS, and ICD-9-CM code sets, etc.) to capture clinical data. However, administrative code sets were designed either to group diagnoses and procedures or to contain broad categories with administrative technical terms with complex rules and guidelines. Examples of this are time durations or vascular branch orders directly stated in various terms.
Administrative codes and terms typically use language that is not natural or familiar for clinicians. For example, in ICD-10-PCS the root operation term “extirpation” is not routinely stated by surgeons. Administrative codes and descriptors also do not contain the different clinical, administrative, and colloquial terms used in healthcare, making it difficult for clinicians, information management professionals, and patients to find the terms they need when performing simple text searches. This disconnect between clinician language and coding sets creates concern over losing clinical intent in the documentation. In addition, forcing a physician to document in administrative terms is uncomfortable and disruptive.
EHR solutions incorporating these terminologies may be limited in providing full value to hospitals and physicians, which may include not delivering meaningful use and full reimbursement levels. These problems may present themselves in various ways. For example, when charting, doctors may be unable to find the correct diagnosis and instead may use free text or may give-up and omit the problem altogether. In turn, this may lead to incomplete and incorrect patient documentation as well as the loss of ability to analyze and report on this information. Lost time and money may result due to under-coding or rejected claims, and the captured information may be useless for meaningful communications with patients and other care providers.
What is needed is a system and method that addresses one or more of the issues and shortcomings presented above.